Local directional ZigZag pattern: A rotation invariant descriptor for texture classification

A New Approach for Effective Retrieval of Medical Images: A Step towards Computer-Assisted Diagnosis

The biomedical imaging field has grown enormously in the past decade. In the era of digitization, the demand for computer-assisted diagnosis is increasing day by day. The COVID-19 pandemic further emphasized how retrieving meaningful information from medical repositories can aid in improving the quality of patient's diagnosis. Therefore, content-based retrieval of medical images has a very prominent role in fulfilling our ultimate goal of developing automated computer-assisted diagnosis systems. Therefore, this paper presents a content-based medical image retrieval system that extracts multi-resolution, noise-resistant, rotation-invariant texture features in the form of a novel pattern descriptor, i.e., MsNrRiTxP, from medical images. In the proposed approach, the input medical image is initially decomposed into three neutrosophic images on its transformation into the neutrosophic domain. Afterwards, three distinct pattern descriptors, i.e., MsTrP, NrTxP, and RiTxP, are derived at multiple scales from the three neutrosophic images. The proposed MsNrRiTxP pattern descriptor is obtained by scale-wise concatenation of the joint histograms of MsTrP×RiTxP and NrTxP×RiTxP. To demonstrate the efficacy of the proposed system, medical images of different modalities, i.e., CT and MRI, from four test datasets are considered in our experimental setup. The retrieval performance of the proposed approach is exhaustively compared with several existing, recent, and state-of-the-art local binary pattern-based variants. The retrieval rates obtained by the proposed approach for the noise-free and noisy variants of the test datasets are observed to be substantially higher than the compared ones.

Local neighborhood gradient pattern: A feature descriptor for content based image retrieval

Local feature descriptors are efficient encoders for capturing repeated local patterns in many of the computer vision applications. Majority of such descriptors consider only limited local neighborhood pixels to encode a pattern. One of the major issues while considering more number of neighborhood pixels is that it increases the dimensionality of the feature descriptor. The proposed descriptor addresses these issues by describing an effective encoding pattern with optimal feature vector length. In this paper, we have proposed Local Neighborhood Gradient Pattern (LNGP) for Content-Based Image Retrieval (CBIR) in which the relationship between a set of neighbours and the centre pixel is considered to obtain a compact 8-bit pattern in the respective pixel position. The relationship of the gradient information of immediate, next-immediate, and diagonal neighbours with the centre pixel is considered for pattern formation, and thus the local information based on pixels in three directions are captured. The experiments are conducted on benchmarked image retrieval datasets such as Wang’s 1K, Corel 5K, Corel 10K, Salzburg (Stex), MIT-Vistex, AT & T, and FEI datasets and it is observed that the proposed descriptor yields average precision of 71.88%, 54.57%, 40.66%, 71.85%, 86.12%, 82.54%, and 68.54% respectively in the mentioned datasets. The comparative analysis of the recent descriptors indicates that the proposed descriptor performs efficiently in CBIR applications.

Cervical cancer diagnosis based on modified uniform local ternary patterns and feed forward multilayer network optimized by genetic algorithm

Cervical cancer is one of the most common types of cancer for women. Early and accurate diagnosis can save the patient's life. Pap smear testing is nowadays commonly used to diagnose cervical cancer. The type, structure and size of the cervical cells in pap smears images are major factors which are used by specialist doctors to diagnosis abnormality. Various image processing-based approaches have been proposed to acquire pap smear images and diagnose cervical cancer in pap smears images. Accuracy is usually the primary objective in evaluating the performance of these systems. In this paper, a two-stage method for pap smear image classification is presented. The aim of the first stage is to extract texture information of the cytoplasm and nucleolus jointly. For this purpose, the pap smear image is first segmented using the appropriate threshold. Then, a texture descriptor is proposed titled modified uniform local ternary patterns (MULTP), to describe the local textural features. Secondly, an optimized multi-layer feed-forward neural network is used to classify the pap smear images. The proposed deep neural network is optimized using genetic algorithm in terms of number of hidden layers and hidden nodes. In this respect, an innovative chromosome representation and cross-over process is proposed to handle these parameters. The performance of the proposed method is evaluated on the Herlev database and compared with many other efficient methods in this scope under the same validation conditions. The results show that the detection accuracy of the proposed method is higher than the compared methods. Insensitivity to image rotation is one of the major advantages of the proposed method. Results show that the proposed method has the capability to be used in online problems because of low run time. The proposed texture descriptor, MULTP is a general operator which can be used in many computer vision problems to describe texture properties of image. Also, the proposed optimization algorithm can be used in deep-networks to improve performance.

3D-local oriented zigzag ternary co-occurrence fused pattern for biomedical CT image retrieval

In this letter, a new feature descriptor called three dimensional local oriented zigzag ternary co-occurrence fused pattern ( 3 D - L O Z T C o F P ) is proposed for computed tomography (CT) image retrieval. Unlike the conventional local pattern based approaches, where the relationship between the reference and its neighbors in a circular shaped neighborhood are captured in a 2-D plane, the proposed descriptor encodes the relationship between the reference and it's neighbors within a local 3D block drawn from multiscale Gaussian filtered images employing a new 3D zigzag sampling structure. The proposed 3D zigzag scan around a reference not only provides an effective texture representation by capturing non-uniform and uniform local texture patterns but the fine to coarse details are also captured via multiscale Gaussian filtered images. In this letter, we have introduced three unique 3D zigzag patterns in four diverse directions. In 3 D - L O Z T C o F P , we first calculate the 3D local ternary pattern within a local 3D block around a reference using proposed 3D zigzag sampling structure at both radius 1 and 2. Then the co-occurrence of similar ternary edges within the local 3D cube is computed to further enhance the discriminative power of the descriptor. A quantization and fusion based scheme is introduced to reduce the feature dimension of the proposed descriptor. Experiments are conducted on popular NEMA and TCIA-CT image databases and the results demonstrate superior retrieval efficiency of the proposed 3 D - L O Z T C o F P descriptor over many local pattern based approaches in terms of average retrieval precision and average retrieval recall in CT image retrieval.

Automatic Land Cover Reconstruction From Historical Aerial Images: An Evaluation of Features Extraction and Classification Algorithms

The land cover reconstruction from monochromatic historical aerial images is a challenging task that has recently attracted an increasing interest from the scientific community with the proliferation of large-scale epidemiological studies involving retrospective analysis of spatial patterns. However, the efforts made by the computer vision community in remote-sensing applications are mostly focused on prospective approaches through the analysis of high-resolution multi-spectral data acquired by the advanced spatial programs. Hence, four contributions are proposed in this paper. They aim at providing a comparison basis for the future development of computer vision algorithms applied to the automation of the land cover reconstruction from monochromatic historical aerial images. First, a new multi-scale multi-date dataset composed of 4.9 million non-overlapping annotated patches of the France territory between 1970 and 1990 has been created with the help of geography experts. This dataset has been named HistAerial. Second, an extensive comparison study of the state-of-the-art texture features extraction and classification algorithms, including deep convolutional neural networks (DCNNs), has been performed. It is presented in the form of an evaluation. Third, a novel low-dimensional local texture filter named rotated-corner local binary pattern (R-CRLBP) is presented as a simplification of the binary gradient contours filter through the use of an orthogonal combination representation. Finally, a novel combination of low-dimensional texture descriptors, including the R-CRLBP filter, is introduced as a light combination of local binary patterns (LCoLBPs). The LCoLBP filter achieved state-of-the-art results on the HistAerial dataset while conserving a relatively low-dimensional feature vector space compared with the DCNN approaches (17 times shorter).